Neural differentiation regulated by biomimetic surfaces presenting motifs of extracellular matrix proteins
Bibliographic record
Abstract
The interaction between cells and the extracellular matrix (ECM) is essential during development. To elucidate the function of ECM proteins on cell differentiation, we developed biomimetic surfaces that display specific ECM peptide motifs in a controlled manner. Presentation of ECM domains for collagen, fibronectin, and laminin influenced the formation of neurites by differentiating PC12 cells. The effect of these peptide sequences was also tested on the development of adult neural stem/progenitor cells. In this system, collagen I and fibronectin induced the formation of beta-III-tubulin positive cells, whereas collagen IV reduced such differentiation. Biomimetic surfaces composed of multiple peptide types enabled the combinatorial effects of various ECM motifs to be studied. Surfaces displaying combined motifs were often predictable as a result of the synergistic effects of ECM peptides studied in isolation. For example, the additive effects of fibronectin and laminin resulted in greater expression of beta-III-tubulin positive cells, whereas the negative effect of the collagen IV domain was canceled out by coexpression of collagen I. However, simultaneous expression of certain ECM domains was less predictable. These data highlight the complexity of the cellular response to combined ECM signals and the need to study the function of ECM domains individually and in combination.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".